{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 29G\r\n",
      "drwxr-xr-x 3 root root 4.0K May  7 02:03 .\r\n",
      "drwxr-xr-x 9 root root 4.0K May  7 11:41 ..\r\n",
      "-rw-r--r-- 1 root root 6.4G May  7 00:57 dblp-2017.json\r\n",
      "-rw-r--r-- 1 root root 8.5G May  7 01:22 dblp-2018.json\r\n",
      "-rw-r--r-- 1 root root 9.3G May  7 02:06 dblp-2019.json\r\n",
      "-rw-r--r-- 1 root root 4.2G May  7 01:52 dblp-2020.json\r\n",
      "drwxr-xr-x 2 root root 4.0K May  7 01:02 .ipynb_checkpoints\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lha ./data/dblp/article/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import codecs\n",
    "import gc\n",
    "import json\n",
    "import psutil\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_available_memory():\n",
    "    pc_mem = psutil.virtual_memory()\n",
    "    div = 1024 ** 3\n",
    "    pc_mem\n",
    "    print(\"available memory: {:.3f} Gb, usage {:.3}%\".format(pc_mem.available/div, 100 - 100.0 * pc_mem.available/pc_mem.total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available memory: 123.819 Gb, usage 1.66%\n"
     ]
    }
   ],
   "source": [
    "show_available_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = list(filter(lambda x: \".json\" in x, os.listdir(\"./data/dblp/article/\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "author2articles = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dblp-2017.json\n",
      "available memory: 123.820 Gb, usage 1.65%\n",
      "available memory: 95.433 Gb, usage 24.2%\n",
      "available memory: 92.759 Gb, usage 26.3%\n",
      "deal with dblp-2017.json costs 129.347 seconds\n",
      "dblp-2018.json\n",
      "available memory: 92.759 Gb, usage 26.3%\n",
      "available memory: 83.093 Gb, usage 34.0%\n",
      "available memory: 83.170 Gb, usage 33.9%\n",
      "deal with dblp-2018.json costs 422.908 seconds\n",
      "dblp-2019.json\n",
      "available memory: 83.170 Gb, usage 33.9%\n",
      "available memory: 80.577 Gb, usage 36.0%\n",
      "available memory: 80.670 Gb, usage 35.9%\n",
      "deal with dblp-2019.json costs 431.915 seconds\n",
      "dblp-2020.json\n",
      "available memory: 80.670 Gb, usage 35.9%\n",
      "available memory: 80.658 Gb, usage 35.9%\n",
      "available memory: 80.781 Gb, usage 35.8%\n",
      "deal with dblp-2020.json costs 274.726 seconds\n"
     ]
    }
   ],
   "source": [
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    start_time = time.time()\n",
    "    show_available_memory()\n",
    "    f = codecs.open(\"./data/dblp/article/{}\".format(filename), \"r\")\n",
    "    content = f.read()\n",
    "    content_json = json.loads(content)\n",
    "    f.close()\n",
    "    del content\n",
    "    gc.collect()\n",
    "    gc.collect()\n",
    "    show_available_memory()\n",
    "    for author in content_json.keys():\n",
    "        temp = content_json[author]\n",
    "        temp_dict = {}\n",
    "        for t_dict in temp:\n",
    "            if \"title\" in t_dict and \"year\" in t_dict:\n",
    "                temp_dict[t_dict['title'].lower()] = t_dict['year']\n",
    "        del temp\n",
    "        author2articles[author] = temp_dict\n",
    "    del content_json\n",
    "    gc.collect()\n",
    "    gc.collect()\n",
    "    show_available_memory()\n",
    "    end_time = time.time()\n",
    "    print(\"deal with {} costs {:.3f} seconds\".format(filename, end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "year2kwinfo = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2245286/2245286 [00:04<00:00, 496747.57it/s]\n"
     ]
    }
   ],
   "source": [
    "with tqdm(author2articles.items(), total=len(author2articles)) as t:\n",
    "    for author, articles in t:\n",
    "        for name, year in articles.items():\n",
    "            if \"complex network\" in name:\n",
    "                temp = year2kwinfo.get(year, [])\n",
    "                temp.append({})\n",
    "                temp[-1][\"author\"] = author\n",
    "                temp[-1][\"article\"] = name\n",
    "                year2kwinfo[year] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available memory: 77.933 Gb, usage 38.1%\n"
     ]
    }
   ],
   "source": [
    "show_available_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1974, 2021))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005\n",
      "[('Matthieu Latapy', 1), ('Jean-Loup Guillaume', 1), ('Masahiro Agu', 1), ('L. Diambra', 1), ('Kaikai Shen', 1), ('Hai-Bo Hu', 1)]\n",
      "2006\n",
      "[('Eocman Lee', 1), ('Ismail Gunes', 1)]\n",
      "2007\n",
      "[('Yuval Elovici', 1), ('Shlomi Dolev', 1), ('Marco Gavanelli', 1), ('Rosa Herrero', 1), ('Jinde Cao', 1), ('Xionglin Luo', 1), ('D.-U. Hwang', 1), ('Massimiliano Cattafi', 1), ('Lucas Antiqueira', 1), ('Xiu-Jie Zhou', 1)]\n",
      "2008\n",
      "[('Subhash Suri', 1), ('Sridhar Rajagopalan', 1), ('Kathy J. Horadam', 1), ('Noga Alon', 1), ('Francesco Sorrentino', 1), ('Gero Schwenk', 1), ('A. Clauset', 1), ('Burak Cetin', 1)]\n",
      "2009\n",
      "[('Luis Antunes', 1), ('Inmaculada Leyva', 1)]\n",
      "2010\n",
      "[('Inmaculada Leyva', 2), ('Zdravko Kacic', 1), ('Mason A. Porter', 1), ('Yi Yang', 1), ('Thomas Rist', 1), ('Jonathan Gratch', 1), ('Michael Bain', 1), ('Liz Sonenberg', 1), ('Patrick G. Kenny', 1), ('Jacob Eisenstein', 1), ('Caetano Traina Jr.', 1), ('Jian-An Fang', 1), ('Carlo Strapparava', 1), ('Andrea L. Bertozzi', 1), ('Wen Sun', 1), ('Michael Z. Q. Chen', 1), ('Wuneng Zhou', 1), ('Miguel Valencia', 1), ('Ana Paula Appel', 1), ('Adriano Arantes Paterlini', 1)]\n",
      "2011\n",
      "[('Witold Dzwinel', 1), ('Feng Chen', 1), ('Jie Cao', 1), ('Witold Pedrycz', 1), ('Yu Chen', 1), ('Qiang Wang', 1), ('Xinwang Liu', 1), ('Yuan Li', 1), ('Mingchu Li', 1), ('Shaojie Qiao', 1), ('Ivan Zelinka', 1), ('Yue Wang', 1), ('Changjie Tang', 1), ('Ruqian Lu', 1), ('Chunyu Yang', 1), ('Haipeng Peng', 1), ('Lusheng Chen', 1), ('Kouichi Sakurai', 1), ('Ka Lok Man', 1), ('Arkadiusz Z. Dudek', 1)]\n",
      "2012\n",
      "[('Peter Kwong-Shun Tam', 1), ('Masahiro Sugimoto', 1), ('Takashi Ikegami', 1), ('Steen Rasmussen', 1), ('David B. Knoester', 1), ('Xiaofeng Liao', 1), ('Wei Huang', 1), ('Chen Chen', 1), ('Vasco Furtado', 1), ('Gheorghe Paun', 1), ('Mohammad-Reza Jahed Motlagh', 1), ('Yuhua Xu', 1), ('Vittorio Loreto', 1), ('Masaru Tomita', 1), ('ZeJun L&uuml;', 1), ('Pei Yu', 1), ('Jian-An Fang', 1), ('Yun Tian', 1), ('Yuxia Li', 1), ('James Worrell', 1)]\n",
      "2013\n",
      "[('Chris Cannings', 1), ('Jianwei Huang', 1), ('V. Judson Harward', 1), ('Philip H. Bailey', 1), ('Richard Southwell', 1), ('James L. Hardison', 1), ('Thangavelu Vijayakumar', 1), ('A. Nirmalkumar', 1), ('Gordon Kohse', 1), ('Yakov Ostrocsky', 1), ('John Bragin', 1), ('Yen-Sheng Chiang', 1), ('Soon Jo Chung', 1), ('Meichun Zhao', 1), ('Sayed Mohammad Almohri', 1), ('Shuzhi Li', 1), ('Zhengzhong Yuan', 1), ('Gerald F. Frasco', 1), ('Patrick L. Harrington Jr.', 1), ('Ajendra Dwivedi', 1)]\n",
      "2014\n",
      "[('Meizhu Li', 2), ('Peter Sanders', 2), ('Yong Deng 0001', 2), ('Hui Chen', 1), ('George Toderici', 1), ('Bin Chen', 1), ('James Cheney', 1), ('Christian Urban', 1), ('Siamak Yousefi', 1), ('Huijun Gao', 1), ('Josef Schicho', 1), ('J&uuml;rgen Kurths', 1), ('M. Alamgir Hossain', 1), ('Beno&icirc;t Champagne', 1), ('Xiao-Wen Chang', 1), ('Jian-An Fang', 1), ('Paolo Rocco', 1), ('Vijayan K. Asari', 1), ('Lingdong Zhao', 1), ('Shaobin Yan', 1)]\n",
      "2015\n",
      "[('Regino Criado', 2), ('Miguel Romance', 2), ('Yang Zhang', 1), ('Jun Zhang', 1), ('Xingshe Zhou', 1), ('Fan Zhang', 1), ('Jian Wan', 1), ('Xiaofeng Liao', 1), ('Gaochao Xu', 1), ('Zhixu Li', 1), ('Xiaodong Fu', 1), ('Yuan Zhang', 1), ('Tong Zhu', 1), ('Dong Sun', 1), ('Andrew McGregor 0001', 1), ('Sergio Greco', 1), ('Bodo Rosenhahn', 1), ('Laura Leal-Taix&eacute;', 1), ('Fabio Martignon', 1), ('Mohammed Feham', 1)]\n",
      "2016\n",
      "[('Henry Leung', 1), ('Li Zhang', 1), ('Qi Liu', 1), ('Gabriel A. Wainer', 1), ('Jian Zhang', 1), ('Zheng Wang', 1), ('Qin Zhang', 1), ('Mohammad Mahfuzul Islam', 1), ('Yuichi Yoshida', 1), ('Lijuan Du', 1), ('Wenjun Xiao', 1), ('Eui-nam Huh', 1), ('Xiaoping Li', 1), ('Yong Chen', 1), ('Huibin Wang', 1), ('Deshi Li', 1), ('Xinlei An', 1), ('Qiaoming Zhu', 1), ('Wei Wei', 1), ('Kai Sun', 1)]\n",
      "2017\n",
      "[('Chika O. Nwankpa', 2), ('Alireza Bagheri', 2), ('Xue-Gang Chen', 2), ('C. Seshadhri', 2), ('Gaoxi Xiao', 2), ('Chai Wah Wu', 2), ('Chia-Chi Chu', 2), ('Jeff Erickson', 1), ('Stefano Leonardi', 1), ('Feng Wang', 1), ('Saket Saurabh', 1), ('Mingyan Liu', 1), ('Stephen Eubank', 1), ('Giovanni De Micheli', 1), ('Luca Benini', 1), ('Bo Li 0001', 1), ('Jason Cong', 1), ('Edward R. Scheinerman', 1), ('Sanjeev Khanna', 1), ('Jae-Bok Song', 1)]\n",
      "2018\n",
      "[('Qing Wang', 2), ('Chen Zhang', 2), ('Chen Chen', 2), ('Liang Gao', 2), ('Jiang-She Zhang', 2), ('Jan Treur', 2), ('Yi-Fan Wang', 2), ('Zhe-Ming Lu', 2), ('Kwok Yip Szeto', 2), ('Huaguang Zhang', 2), ('Chengyi Xia', 2), ('Wenxue Li', 2), ('Chun-Xia Zhang', 2), ('Olivier Gascuel', 1), ('Xiaowen Chu', 1), ('Li Zhang', 1), ('Yan Liu', 1), ('Mourad Debbabi', 1), ('Yun Chi', 1), ('Paolo Terenziani', 1)]\n",
      "2019\n",
      "[('Yong Deng', 3), ('Yong Deng 0001', 3), ('Boleslaw K. Szymanski', 2), ('Shouyang Wang', 2), ('Guojin He', 2), ('Hongsheng Qi', 2), ('Lin Wang', 2), ('Yang Guo', 2), ('Wei Liu', 2), ('Yiguang Hong', 2), ('Christian Schlegel', 2), ('Wei Xing Zheng', 2), ('Weidong Qiu', 2), ('Tuvi Etzion', 2), ('Nicola Marchetti', 2), ('Hongye Su', 2), ('Peng Shi 0001', 2), ('Wenxue Li', 2), ('Elizabeth Munch', 2), ('Delfina Malandrino', 2)]\n",
      "2020\n",
      "[('Witold Pedrycz', 2), ('Bin Deng', 2), ('Rongxing Lu', 2), ('Shouming Zhong', 2), ('Huiyan Li', 2), ('Kathleen M. Carley', 2), ('Jin-Liang Wang', 2), ('Jiang Wang 0002', 2), ('Weili Wu', 1), ('Dong Xu', 1), ('Vincent Mousseau', 1), ('Yan Liu', 1), ('Ping Wang', 1), ('Meng Wang', 1), ('Guandong Xu', 1), ('Yang Du', 1), ('Joshua Zhexue Huang', 1), ('Henrik Jeldtoft Jensen', 1), ('Hideki Takayasu', 1), ('Tao Zhou', 1)]\n"
     ]
    }
   ],
   "source": [
    "years = [int(key) for key in year2kwinfo.keys()]\n",
    "years = sorted(years)\n",
    "for key in years:\n",
    "    author2count = {}\n",
    "    key = str(key)\n",
    "    for temp in year2kwinfo[key]:\n",
    "        c = author2count.get(temp[\"author\"], 0)\n",
    "        c += 1\n",
    "        author2count[temp[\"author\"]] = c\n",
    "    print(key)\n",
    "    print(sorted(author2count.items(), key= lambda x: x[1], reverse= True)[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = []\n",
    "y = []\n",
    "for key in year2kwinfo.keys():\n",
    "    x.append(int(key))\n",
    "    article_ = set()\n",
    "    for temp in year2kwinfo[key]:\n",
    "        article_.add(temp[\"article\"].lower())\n",
    "    y.append(len(article_))\n",
    "indices = np.argsort(x).tolist()\n",
    "x = [x[index] for index in indices[:-1]]\n",
    "y = [y[index] for index in indices[:-1]]\n",
    "plt.style.use('seaborn-notebook')\n",
    "plt.figure(figsize=(10, 8), dpi= 300)\n",
    "plt.plot(x, y, 'o-', color=\"skyblue\")\n",
    "plt.ylim(0)\n",
    "plt.title('Complex Network occurrence count vs year')\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"occurrence count\")\n",
    "plt.savefig(\"./images/complexNetworkCount.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"./data/dblp/author2articles\", exist_ok= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = codecs.open(\"./data/dblp/author2articles/data.json\", \"w\")\n",
    "content = json.dumps(author2articles, ensure_ascii=False)\n",
    "f.write(content)\n",
    "del content\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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